{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  一、案例简介\n",
    "## 1. 案例背景\n",
    ">   随着时代的进步，航空航天作为我国的战略性发展事业，取得了极大成就。航天事业的发展核心是飞行安全，飞行安全既是飞行人员与乘客的生命安全保障，又是航天科技发展的方向和目标。气象条件对飞行的影响是不同的，也是不可避免的，航天部门应对飞行威胁较大的恶劣天气进行分析，采取相应的对策进行防范，不断提高飞行的安全性与可靠性。\n",
    ">   恶劣天气的类型很多，主要包括大风、云、雷电、暴雨、冰雪，另外，大雾、风切变、冰高空急流等也会对飞行安全产生不同程度的影响。恶劣天气的影响具有不确定性，因而应减少在恶劣天气范围内进行飞行。\n",
    ">   通过天气因素上座数量的分析，预测不同天气情况下航班的上座情况，进而调整飞行计划以应对不同天气带来的影响。\n",
    "\n",
    "## 2. 案例意义\n",
    ">   从分析结果将使航空公司依据天气模式调整飞行时间表。它也可以引导乘客做出新的选择。航空公司可以提前几天预测到未来航班上座情况，然后未雨绸缪地进行调整。航空公司也可以预先发出警告更有效分配自己的资源、地勤人员、飞行人员和其他资产以减少损失\n",
    "## 3. 业务图\n",
    "![业务图](image-001.png)\n",
    "## 4. 案例目标\n",
    ">     现在，大多数航班只能对连续进行重复，一般处理天气延误的航班是在其发生之后。我们的大数据能够让他们预先预测延迟，以更好地与乘客沟通，以及优化资源。通过数据分析，预测假设航班和不同天气情况之间的结果，帮助迅速提前发现因天气发生的上座影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、相关技术\n",
    "- ## 数据采集，通过pymysql连接mysql数据库获取数据 \n",
    "- ## 数据预处理，通过pandas、sklearn进行数据清洗，转换等预处理\n",
    "- ## 数据探索，通过matplotlib及pyecharts进行数据可视化，发现数据规律\n",
    "- ## 数据分析建模，通过机器学习算法库sklearn学习已有数据规律建模，预测指定航班在不同天气下的上座率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、案例步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 1. 数据采集: 从数据库casepro中获取数据表t_plane_order\\t_plane_weather,将表格转存在本地csv并展示数据\n",
    "\n",
    "> 数据库信息： host：10.102.52.248，port=3306， user=\"root\", passwd=\"root\"\n",
    "\n",
    "> 航班订单数据，包含航班时刻表，航班号，子订单，飞行日期，头等舱，公务舱，经济舱，其他，头等舱总数，公务舱总数，经济舱总数，其他总数。\n",
    "\n",
    "> 天气数据，包含基线：日期，高温，低温，天气状况，风，空气"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql #导入模块\n",
    "import pandas as pd\n",
    "\n",
    "#远程连接数据库\n",
    "db = pymysql.connect(\n",
    "         host='10.102.52.248',\n",
    "         port=3306,\n",
    "         user='root',\n",
    "         passwd='root',\n",
    "         db='casepro',\n",
    "         charset='utf8'\n",
    "         )\n",
    "def link_mysql(db):\n",
    "    #使用cursor()方法获取操作游标 \n",
    "    cursor = db.cursor()\n",
    "    sql = \"\"\"SELECT * FROM `t_plane_order`\"\"\"\n",
    "    sql1 = \"\"\"SELECT * FROM `t_plane_weather`\"\"\"\n",
    "    try:\n",
    "        cursor.execute(sql)  # 执行sql语句\n",
    "        result = cursor.fetchall()\n",
    "        columns = ['航班时刻表','航班号','子订单','日期','头等舱','公务舱','经济舱','其他','头等舱总数','公务舱总数','经济舱总数','其他总数']\n",
    "        frame = pd.DataFrame(list(result),columns=columns)\n",
    "        frame.to_csv(\"D:/webdownload/实训/航班天气因素上座情况预测分析案例.csv\",encoding='utf-8')\n",
    "        cursor.execute(sql1)  # 执行sql语句\n",
    "        result1 = cursor.fetchall()\n",
    "        columns_weather = ['日期','高温','低温','天气状况','风','空气']\n",
    "        frame_weather = pd.DataFrame(list(result1),columns=columns_weather)\n",
    "        frame_weather.to_csv(\"D:/webdownload/实训/天气数据.csv\",encoding='utf-8')\n",
    "    except Exception:\n",
    "        db.rollback()  # 发生错误时回滚\n",
    "        print(\"查询失败\")\n",
    "    cursor.close()  # 关闭游标\n",
    "    db.close()  \n",
    "link_mysql(db)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 2、数据预处理（清洗、数值化转换、标准化等）\n",
    "\n",
    "> 空数据判断和处理（注意分析空占比，区分数值型和类别型）\n",
    "\n",
    "> 数据规范性检查（格式、范围等）\n",
    "\n",
    "> 去除不规范数据、无效、无分析价值数据等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:37: DtypeWarning: Columns (3,5) have mixed types.Specify dtype option on import or set low_memory=False.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各字段的空占比为：\n",
      "子订单      0.272959\n",
      "航班号      0.013529\n",
      "日期       0.000389\n",
      "航班时刻表    0.000000\n",
      "头等舱      0.000000\n",
      "公务舱      0.000000\n",
      "经济舱      0.000000\n",
      "其他       0.000000\n",
      "头等舱总数    0.000000\n",
      "公务舱总数    0.000000\n",
      "经济舱总数    0.000000\n",
      "其他总数     0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#对航班数据进行清理\n",
    "def yucli_plane():\n",
    "    df_plane = pd.read_csv('航班天气因素上座情况预测分析案例.csv',encoding='utf-8')\n",
    "    df_plane = pd.DataFrame(data=df_plane)\n",
    "    df_plane = df_plane.drop(columns='Unnamed: 0')\n",
    "    #判断其空占比\n",
    "    df_plane_percent = df_plane.isna().sum().sort_values(ascending=False) / len(df_plane)\n",
    "    print(\"各字段的空占比为：\\n{}\".format(df_plane_percent))\n",
    "    #删除空数据\n",
    "    df_plane_notNu = df_plane.dropna(axis=0)\n",
    "    #删除重复数据\n",
    "    df_plane_notNu = df_plane_notNu.drop_duplicates()\n",
    "    #将str类型改为float\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i] = df_plane_notNu.iloc[:,i].astype(float)\n",
    "    #将负值替换成零\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i][df_plane_notNu.iloc[:,i] < 0] = 0.0\n",
    "    #删除日期中不规范的数据\n",
    "    df_plane_notNu[df_plane_notNu.iloc[:,3] == \"0.0\"] = np.nan\n",
    "    df_plane_notNu.dropna(axis=0)\n",
    "    return df_plane_notNu\n",
    "\n",
    "#对天气数据进行处理\n",
    "def yucli_weath():\n",
    "    df_weath = pd.read_csv('天气数据.csv',encoding='utf-8')\n",
    "    df_weath = pd.DataFrame(df_weath)\n",
    "    #删除无分析价值数据\n",
    "    df_weath = df_weath.drop('Unnamed: 0',axis=1)\n",
    "    #将日期和空气格式化\n",
    "    df_weath['日期'] = df_weath['日期'].apply(lambda x:str(x)[0:10])\n",
    "    df_weath['空气'] = df_weath['空气'].apply(lambda x:str(x)[:-1])\n",
    "    return df_weath\n",
    "df_result = pd.merge(left=yucli_plane(), right=yucli_weath(), on=\"日期\", how=\"inner\")\n",
    "df_result = df_result.replace(['中雨~小雨','多云~中雨','多云~小雨','多云~晴','多云~阴','多云~雷阵雨','多云~霾','大暴雨~中雨','大雨~多云','小雨~中雨','小雨~多云','小雨~大雨','小雨~小到中雨','小雨~晴','小雨~阴','小雪~多云','小雪~阴','晴~多云','晴~小雨','晴~阴','晴~霾','阴~多云','阴~小雨','阴~晴','阴~阵雨','阴~雷阵雨','阵雨~多云','阵雨~阴','雨夹雪~大雪','雷阵雨~阵雨','霾~多云','霾~小雨','霾~晴','霾~阴','霾~阵雨','霾~雨夹雪','霾~雷阵雨','霾~雾'],['小雨','中雨','小雨','晴','阴','雷阵雨','霾','中雨','中雨','小雨','小雨','中雨','小雨','晴','阴','多云','阴','晴','晴','阴','霾','阴','小雨','阴','阴','雷阵雨','阵雨','阵雨','大雪','雷阵雨','多云','小雨','晴','阴','阵雨','雨夹雪','雷阵雨','雾'])\n",
    "df_result1=df_result.groupby('天气状况').sum()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 3、数据探索，发现数据中的规律"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3600x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "from matplotlib.lines import lineStyles\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "matplotlib.pyplot.figure(figsize=(20,10),dpi=180)\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "#把所有舱人数总和\n",
    "df_result1['上座人数']=df_result1[['公务舱','头等舱','经济舱','其他']].sum(axis=1)\n",
    "\n",
    "\n",
    "x=df_result1.index.values\n",
    "\n",
    "y2=df_result1['上座人数']\n",
    "sns.barplot(x,y2,label='上座人数')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_result1['总座位数']=df_result1[['头等舱总数','公务舱总数','经济舱总数','其他总数']].sum(axis=1)\n",
    "y1=(df_result1['上座人数']/df_result1['总座位数'])*100\n",
    "sns.lineplot(x,y1,label='上座率')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 4、数据分析建模并评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 5、整理工程并部署：输入航班号、根据未来一周的天气（网上爬取的天气预报），预测并输出其上座率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series\n",
    "import pandas as pd\n",
    "from numpy import NaN\n",
    "series_obj = Series([None, 4, NaN])\n",
    "pd.isnull(series_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.4 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "840b83a840664c52454f0735a8442d1647f000adfcd65e5ae08b9078efb1afec"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
